This invention relates in general to Multi Input Multi Output (MIMO) 2-way Wireless Communication Systems. More specifically, this invention is related to MIMO 2-way Wireless Communication Systems where Multiple Antenna Arrays (MAA) are used in the system communication devices, where the propagation Channel Matrix is random, and where Channel State Information is not necessarily available at the Transmitter Side.
During recent years MIMO schemes were proposed for Wireless Communication Systems whereby the system communication devices include MAA and associated transmission and reception circuitry. MIMO systems, when operated in richly scattering channels (such as typical urban and indoor channels in conjunction with properly designed antenna arrays) may exhibit space diversity gain (and thus Extended Range), and, in certain cases, the ability to effectively multiply the overall data rate transferred thru the channel by means of splitting the data stream into several sub-streams, all transmitted simultaneously (and thus achieving Extended Rate). Note that while the term Extended Capacity could be used instead of both Extended Range/Rate (since in Wireless Communication Systems range can be usually traded off by rate and vice-versa) we prefer the latter distinction along this text to distinguish between the ‘conventional’ diversity gain on one hand, and the ability to transmit several data sub-streams in parallel, which may be achieved only when the 2 communicating sides include an MAA, on the other.
These MIMO schemes may be broadly classified into 2 classes: those that require Channel State Information (CSI) to be available at the Transmitting Side (e.g. [4], [17]) prior to data sub-streams burst transmission and those that do not necessarily require that any CSI be available at the Transmitting Side. This proposed invention deals in particular with MIMO schemes that belong to this second class.
The governing equation of said class of MIMO schemes isyc=Hcxc+nc  (1)where, in complex base-band representation, Hc denotes the propagation complex Channel Matrix of Rc×Lc elements (the ‘c’ subscript denoting here and elsewhere the complex nature of these entities), with Rc and Lc the number of antenna elements at the (receiving) Right and (transmitting) Left sides respectively, where xc is the transmitted complex base-band vector, yc is the received complex base-band vector distorted by the channel Hc and interfered by the channel additive noise nc.
It is assumed in the sequel for purpose of simplicity and without loss of generalization that the Channel Matrix elements are complex Gaussian random variables with zero mean and unit variance (E[hij]=0; E[|hij|2]=1); that the number of simultaneous complex sub-streams Mc satisfies Mc≦min{Lc, Rc}; that the transmission vector elements xci are uniformly distributed, derived (as will be shown in the sequel) from a ‘typical’ constellation (such as QPSK, 16-QAM, 64-QAM, etc.); that the transmission power is constrained to unity (E[xc′xc]=1, where (.)′ denotes the conjugate transpose operation), and that the channel additive noise nc is a complex Gaussian random variable with zero mean, i.i.d. elements and variance inversely proportional to the Receiver mean Signal to Noise Ratio ( SNR) or mean Symbol Energy to Noise Power Density ( Es/No) ([e.g. [5]), i.e. E[nc]=0, E[nci′nci]=1/ SNR=2/(Mc. ( Es/No)).
It is further assumed in the sequel that the Channel Matrix Hc is estimated at the Receiving Side prior to data sub-streams reception by some implicit Channel training stage, i.e. Hcn≅Hc where Hcn denotes the estimated Channel Matrix and that during the duration of a transmission burst the Channel Matrix Hc undergoes no or small changes.
Several solutions have been proposed to the MIMO equation (1) above. In one such proposed solution the transmitted vector xc is estimated by application of the Inverse Channel matrix (or, as is usually more appropriate, the pseudo-inverse matrix), followed by a slicing (or decision, or ro unding) operation omitted herein for simplicity. This proposed method is also known as the Zero Forcing (ZF) method, is equivalent to the Least Square Estimate (LSE) method, and is considered ‘naïve’ (due to its relatively poor Symbol Error Rate (SER) vs Es/No performance), but is widely used in literature (as well as herein) as a performance reference scheme. In formal notationxcZF=(Hcn′Hcn)−1Hcn′yc  (2)where xcZF denotes the estimator of the transmitted sub-streams vector xc. A variant of this ZF method, Minimum Mean Square Estimate (MMSE) with somewhat better performance and higher complexity is used sometimes, and omitted herein for the sake of brevity. The symbol processing complexity of this ZF scheme can be easily shown to be quadratic with the dimension of the transmitted vector xc and independent of the constellation order (QPSK, 16-QAM, etc.)
Another proposed solution (e.g. [1]) to (1) above is based on an Interference Cancellation scheme whereby sub-streams are usually ordered (according to some quality criterion) and then individually estimated, sliced and cancelled out from the sub-sequent sub-streams. This Interference Cancellation method yields better performance than (2) above, at a significant complexity penalty.
Still other proposed solution (e.g. [2]) to (1) above consists of estimating each sub-stream quality (e.g. by monitoring its SER), sending quality feedback from Receiver Side to Transmitter Side, and individually adapting the Transmitter sub-streams parameters (such as constellation type, channel code, etc.) so as to maximize the system throughput.
Still other proposed solution (e.g. [3]) to (1) above, of special relevance to our proposed invention, is the so called Maximum Likelihood Detection (MLD) scheme whereby the estimated transmitted vector xcMLD is the vector which minimizes the (Euclidean) distance between the received vector yc and the Channel transformed vector (Hcnxc), namely:xcMLD=arg min ∥yc−Hcnxci∥2  (3)where the minimization process takes place upon all the candidate vectors xci (not to be confused with the transmission vector elements xci). This scheme is known to be optimal in the sense of SER vs. Es/No performance, and thus serves as a lower bound reference in literature (as well as herein). However the MLD scheme is also known (again, [3]) to be of prohibitive complexity, the number of candidate vectors xci exponentially growing with both the dimension of xc and the order of the symbol constellation; for example, the detection of a transmitted vector with Mc=4 sub-streams (i.e. a MIMO system comprising 4 radiating elements MAA on each side) drawn out of 64-QAM constellation, would require 26×4 or approximately 16000000 operations/symbol, clearly prohibitive with present art computation devices technology.
Still other proposed solution to (1) above consists of simplifications to (3) above whereby the candidates search set is reduced to a smaller region, typically a ‘sphere’ of fixed or adaptive radius around the received vector yc. This class of methods is sometimes appropriately named Sphere Decoding (SD for shorthand, e.g. [14]). While providing a lower complexity solution than exhaustive search as per (3) above, their complexity is still prohibitive for many cases of practical interest. In fact no paper seems to have been published describing SD implementations for such relevant configurations as 64-QAM, Mc=4.